SQL Injection Detection Using Machine Learning Techniques and Multiple Data Sources

2018 ◽  
Author(s):  
Kevin Ross
Keyword(s):  
Machine Learning ◽  
Data Sources ◽  
Machine Learning Techniques ◽  
Sql Injection ◽  
Multiple Data Sources ◽  
Multiple Data ◽  
Learning Techniques

scholarly journals Leveraging Artificial Intelligence and Big Data to Optimize COVID-19 Clinical Public Health and Vaccination Roll-Out Strategies in Africa

2021 ◽  
Vol 18 (15) ◽  
pp. 7890
Author(s):  
Bruce Mellado ◽  
Jianhong Wu ◽  
Jude Dzevela Kong ◽  
Nicola Luigi Bragazzi ◽  
Ali Asgary ◽  
...  
Keyword(s):  
Public Health ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Developed Countries ◽  
Machine Learning Techniques ◽  
Multiple Data ◽  
Learning Techniques ◽  
Unequal Access ◽  
Roll Out

COVID-19 is imposing massive health, social and economic costs. While many developed countries have started vaccinating, most African nations are waiting for vaccine stocks to be allocated and are using clinical public health (CPH) strategies to control the pandemic. The emergence of variants of concern (VOC), unequal access to the vaccine supply and locally specific logistical and vaccine delivery parameters, add complexity to national CPH strategies and amplify the urgent need for effective CPH policies. Big data and artificial intelligence machine learning techniques and collaborations can be instrumental in an accurate, timely, locally nuanced analysis of multiple data sources to inform CPH decision-making, vaccination strategies and their staged roll-out. The Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC) has been established to develop and employ machine learning techniques to design CPH strategies in Africa, which requires ongoing collaboration, testing and development to maximize the equity and effectiveness of COVID-19-related CPH interventions.

Early Intervention Systems: Predicting Adverse Interactions Between Police and the Public

2017 ◽  
Vol 29 (2) ◽  
pp. 190-209 ◽  
Author(s):  
Jennifer Helsby ◽  
Samuel Carton ◽  
Kenneth Joseph ◽  
Ayesha Mahmud ◽  
Youngsoo Park ◽  
...  
Keyword(s):  
Machine Learning ◽  
Early Intervention ◽  
Police Department ◽  
Data Sources ◽  
Data Driven ◽  
Machine Learning Techniques ◽  
Police Legitimacy ◽  
The Public ◽  
Learning Techniques ◽  
Improve Accuracy

Adverse interactions between police and the public hurt police legitimacy, cause harm to both officers and the public, and result in costly litigation. Early intervention systems (EISs) that flag officers considered most likely to be involved in one of these adverse events are an important tool for police supervision and for targeting interventions such as counseling or training. However, the EISs that exist are not data-driven and based on supervisor intuition. We have developed a data-driven EIS that uses a diverse set of data sources from the Charlotte-Mecklenburg Police Department and machine learning techniques to more accurately predict the officers who will have an adverse event. Our approach is able to significantly improve accuracy compared with their existing EIS: Preliminary results indicate a 20% reduction in false positives and a 75% increase in true positives.

SQL Injection Detection Using Machine Learning Techniques

2021 ◽  
Author(s):  
Eman Hosam ◽  
Hagar Hosny ◽  
Walaa Ashraf ◽  
Ahmed S. Kaseb
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Sql Injection ◽  
Learning Techniques

scholarly journals An Improved SQL Injection Attack Detection Model Using Machine Learning Techniques

2021 ◽  
Vol 11 (1) ◽  
pp. 53-57
Author(s):  
Yazeed Abdulmalik
Keyword(s):  
Machine Learning ◽  
Web Application ◽  
Attack Detection ◽  
Machine Learning Techniques ◽  
Detection Methods ◽  
Semantic Features ◽  
Sql Injection ◽  
Detection Model ◽  
Learning Techniques ◽  
Sql Injection Attack

SQL Injection Attack (SQLIA) is a common cyberattack that target web application database. With the ever increasing and varying techniques to exploit web application SQLIA vulnerabilities, there is no a comprehensive method that can solve this kind of attacks. Therefore, these various of attack techniques required to establish many methods against in order to mitigate its threats. However, most of these methods have not yet been evaluated, where it is still just theories and require to implement and measure its performance and set its limitation. Moreover, most of the existing SQL injection countermeasures either used syntax-based detection methods or a list of predefined rules to detect the SQL injection, which is vulnerable in advance and sophisticated type of attacks because attackers create new ways to evade the detection utilizing their pre-knowledge. Although semantic-based features can improve the detection, up to our knowledge, no studies focused on extracting the semantic features from SQL stamens. This paper, investigates a designed model that can improve the efficacy of the SQL injection attack detection using machine learning techniques by extracting the semantic features that can effectively indicate the SQL injection attack. Also, a tenfold approach will be used to evaluate and validate the proposed detection model.

scholarly journals Analysed potential of big data and supervised machine learning techniques in effectively forecasting travel times from fused data

2015 ◽  
Vol 27 (6) ◽  
pp. 515-528 ◽  
Author(s):  
Ivana Šemanjski
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Random Forest ◽  
Data Sources ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Travel Times ◽  
The Road ◽  
Learning Techniques ◽  
Nearest Neighbours

Travel time forecasting is an interesting topic for many ITS services. Increased availability of data collection sensors increases the availability of the predictor variables but also highlights the high processing issues related to this big data availability. In this paper we aimed to analyse the potential of big data and supervised machine learning techniques in effectively forecasting travel times. For this purpose we used fused data from three data sources (Global Positioning System vehicles tracks, road network infrastructure data and meteorological data) and four machine learning techniques (k-nearest neighbours, support vector machines, boosting trees and random forest). To evaluate the forecasting results we compared them in-between different road classes in the context of absolute values, measured in minutes, and the mean squared percentage error. For the road classes with the high average speed and long road segments, machine learning techniques forecasted travel times with small relative error, while for the road classes with the small average speeds and segment lengths this was a more demanding task. All three data sources were proven itself to have a high impact on the travel time forecast accuracy and the best results (taking into account all road classes) were achieved for the k-nearest neighbours and random forest techniques.

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